Smartphone-Based Indoor Localization System Using Wi-Fi RTT/Magnetic/PDR Based on an Improved Particle Filter

In this article, we have constructed a high-precision indoor positioning system by leveraging Wi-Fi round-trip time (RTT), magnetic field, and pedestrian dead reckoning (PDR). For the Wi-Fi RTT module, a collaborative Wi-Fi RTT positioning approach is proposed by combining RTT ranging and fingerprinting through a Cramer-Rao lower bound (CRLB)-based outlier detection method. Based on this, an enhanced version of the geomagnetic-aided Wi-Fi RTT (GAWR) localization method is implemented. For better smartphone-based deployment, we improve PDR in terms of step detection, step length estimation, and heading optimization. Moreover, an optimization strategy incorporating map-aided resampling and particle swarm reconstruction is designed to enhance the performance of particle filter (PF). Finally, the Wi-Fi RTT, magnetic positioning, and PDR are integrated based on the improved PF (IPF). Our system employs two mechanisms to achieve fusion localization, which means that the system executes Wi-Fi/magnetic/PDR fusion when the Wi-Fi module is available and switches to magnetic/PDR fusion when the Wi-Fi signal is absent. Extensive experiments conducted in a corridor environment demonstrate that the IPF-based Wi-Fi RTT/magnetic/PDR model achieves the localization accuracy of 0.64–0.91 m when the Wi-Fi module is available and unavailable, respectively, showing meter-level continuous positioning capability even in the presence of additional Wi-Fi ranging errors and in areas without Wi-Fi signal coverage.